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Optimal configuration of remanufacturing supply network with return quality decision

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  • Mohannad Radhi
  • Guoqing Zhang

Abstract

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.

Suggested Citation

  • Mohannad Radhi & Guoqing Zhang, 2016. "Optimal configuration of remanufacturing supply network with return quality decision," International Journal of Production Research, Taylor & Francis Journals, vol. 54(5), pages 1487-1502, March.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:5:p:1487-1502
    DOI: 10.1080/00207543.2015.1086034
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    Cited by:

    1. Tian, Xiaoyu & Zhang, Zhi-Hai, 2019. "Capacitated disassembly scheduling and pricing of returned products with price-dependent yield," Omega, Elsevier, vol. 84(C), pages 160-174.
    2. Ebrahimi Bajgani, Sahar & Saberi, Sara & Toyasaki, Fuminori, 2023. "Designing a reverse supply chain network with quality control for returned products: Strategies to mitigate free-riding effect and ensure compliance with technology licensing requirements," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    3. Ponte, Borja & Cannella, Salvatore & Dominguez, Roberto & Naim, Mohamed M. & Syntetos, Aris A., 2021. "Quality grading of returns and the dynamics of remanufacturing," International Journal of Production Economics, Elsevier, vol. 236(C).
    4. Yicong Gao & Shanhe Lou & Hao Zheng & Jianrong Tan, 2023. "A data-driven method of selective disassembly planning at end-of-life under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 565-585, February.
    5. Qingguo Bai & Jianteng Xu & Yuzhong Zhang, 2022. "The distributionally robust optimization model for a remanufacturing system under cap-and-trade policy: a newsvendor approach," Annals of Operations Research, Springer, vol. 309(2), pages 731-760, February.
    6. Suvadarshini, Pinakhi & Biswas, Indranil & Srivastava, Samir K., 2023. "Impact of reverse channel competition, individual rationality, and information asymmetry on multi-channel closed-loop supply chain design," International Journal of Production Economics, Elsevier, vol. 259(C).

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